Spectral salient object detection
Paper in proceeding, 2014

Many existing methods for salient object detection are performed by over-segmenting images into non-overlapping regions, which facilitate local/global color statistics for saliency computation. In this paper, we propose a new approach: spectral salient object detection, which is benefited from selected attributes of normalized cut, enabling better retaining of holistic salient objects as comparing to conventionally employed pre-segmentation techniques. The proposed saliency detection method recursively bi-partitions regions that render the lowest cut cost in each iteration, resulting in binary spanning tree structure. Each segmented region is then evaluated under criterion that fit Gestalt laws and statistical prior. Final result is obtained by integrating multiple intermediate saliency maps. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method against 13 state-of-the-art approaches to salient object detection.

Gestalt laws


Normalized cut

Salient object detection



Keren Fu

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

C Gong

Shanghai Jiao Tong University

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Jie Yang

Shanghai Jiao Tong University

Xiangjian He

University of Technology Sydney

Proceedings - IEEE International Conference on Multimedia and Expo

19457871 (ISSN) 1945788X (eISSN)

Vol. 2014-September Septmber 6- 6890142
978-1-4799-4761-4 (ISBN)

Areas of Advance

Information and Communication Technology


Subject Categories

Information Science

Signal Processing

Computer Vision and Robotics (Autonomous Systems)





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